Using function approximation for personalized point-of-interest recommendation

Point-of-interest (POI) recommender system encourages users to share their locations and social experience through check-ins in online location-based social networks. A most recent algorithm for POI recommendation takes into account both the location relevance and diversity. The relevance measures users’ personal preference while the diversity considers location categories. There exists a dilemma of weighting these two factors in the recommendation. The location diversity is weighted more when a user is new to a city and expects to explore the city in the new visit. In this paper, we propose a method to automatically adjust the weights according to user’s personal preference. We focus on investigating a function between the number of location categories and a weight value for each user, where the Chebyshev polynomial approximation method using binary values is applied. We further improve the approximation by exploring similar behavior of users within a location category. We conduct experiments on five real-world datasets, and show that the new approach can make a good balance of weighting the two factors therefore providing better recommendation

Group sparse optimization for learning predictive state representations

Predictive state representations (PSRs) are a commonly used approach for agents to summarize the information from history generated during their interaction with a dynamical environment and the agents may use PSRs to predict the future observation. Existing works have shown the benefits of PSRs for modelling partially observable dynamical systems. One of the key issues in PSRs is to discover a set of tests for representing states, which is called core tests. However, there is no very efficient technique to find the core tests for a large and complex problem in practice. In this paper, we formulate the discovering of the set of core tests as an optimization problem and exploit a group sparsity of the decision-making matrix to solve the problem. Then the PSR parameters can be obtained simultaneously. Hence, the model of the underlying system can be built immediately. The new learning approach doesn’t require the specification of the number of core tests. Furthermore, the embedded optimization method for solving the considered group Lasso problem, called alternating direction method of multipliers (ADMM), can achieve a global convergence. We conduct experiments on three problem domains including one extremely large problem domain and show promising performances of the new approach

Navigating Discrete Difference Equation Governed WMR by Virtual Linear Leader Guided HMPC

In this paper, we revisit model predictive control (MPC) for the classical wheeled mobile robot (WMR) navigation problem. We prove that the reachable set based hierarchical MPC (HMPC), a state-of-the-art MPC, cannot handle WMR navigation in theory due to the non-existence of non-trivial linear system with an under-approximate reachable set of WMR. Nevertheless, we propose a virtual linear leader guided MPC (VLL-MPC) to enable HMPC structure. Different from current HMPCs, we use a virtual linear system with an under-approximate path set rather than the traditional trace set to guide the WMR. We provide a valid construction of the virtual linear leader. We prove the stability of VLL-MPC, and discuss its complexity. In the experiment, we demonstrate the advantage of VLL-MPC empirically by comparing it with NMPC, LMPC and anytime RRT* in several scenarios

Navigating Discrete Difference Equation Governed WMR by Virtual Linear Leader Guided HMPC

In this paper, we revisit model predictive control (MPC) for the classical wheeled mobile robot (WMR) navigation problem. We prove that the reachable set based hierarchical MPC (HMPC), a state-of-the-art MPC, cannot handle WMR navigation in theory due to the non-existence of non-trivial linear system with an under-approximate reachable set of WMR. Nevertheless, we propose a virtual linear leader guided MPC (VLL-MPC) to enable HMPC structure. Different from current HMPCs, we use a virtual linear system with an under-approximate path set rather than the traditional trace set to guide the WMR. We provide a valid construction of the virtual linear leader. We prove the stability of VLL-MPC, and discuss its complexity. In the experiment, we demonstrate the advantage of VLL-MPC empirically by comparing it with NMPC, LMPC and anytime RRT* in several scenarios

Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.Mohamed Elgendi, Björn Eskofier, Socrates Dokos, Derek Abbot

Development and evaluation of a deep learning model for automatic segmentation of non-perfusion area in fundus fluorescein angiography

Diabetic retinopathy (DR) is the most prevalent cause of preventable vision loss worldwide, imposing a significant economic and medical burden on society today, of which early identification is the cornerstones of the management. The diagnosis and severity grading of DR rely on scales based on clinical visualized features, but lack detailed quantitative parameters. Retinal non-perfusion area (NPA) is a pathogenic characteristic of DR that symbolizes retinal hypoxia conditions, and was found to be intimately associated with disease progression, prognosis, and management. However, the practical value of NPA is constrained since it appears on fundus fluorescein angiography (FFA) as distributed, irregularly shaped, darker plaques that are challenging to measure manually. In this study, we propose a deep learning-based method, NPA-Net, for accurate and automatic segmentation of NPAs from FFA images acquired in clinical practice. NPA-Net uses the U-net structure as the basic backbone, which has an encoder-decoder model structure. To enhance the recognition performance of the model for NPA, we adaptively incorporate multi-scale features and contextual information in feature learning and design three modules: Adaptive Encoder Feature Fusion (AEFF) module, Multilayer Deep Supervised Loss, and Atrous Spatial Pyramid Pooling (ASPP) module, which enhance the recognition ability of the model for NPAs of different sizes from different perspectives. We conducted extensive experiments on a clinical dataset with 163 eyes with NPAs manually annotated by ophthalmologists, and NPA-Net achieved better segmentation performance compared to other existing methods with an area under the receiver operating characteristic curve (AUC) of 0.9752, accuracy of 0.9431, sensitivity of 0.8794, specificity of 0.9459, IOU of 0.3876 and Dice of 0.5686. This new automatic segmentation model is useful for identifying NPA in clinical practice, generating quantitative parameters that can be useful for further research as well as guiding DR detection, grading severity, treatment planning, and prognosis.</p

Analysis of Patents Issued in China for Antihyperglycemic Therapies for Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is prevalent, with a dramatic increase in recent years. Moreover, its microvascular and macrovascular complications cause significant societal issues. The demand for new and effective antidiabetic therapies grows with each passing day and motivates organizations and individuals to pay more attention to such products. In this article, we focused on oral antihyperglycemic drugs patented in China and introduced them according to their antihyperglycemic mechanisms. By searching the website of State Intellectual Property Office of the People’s Republic of China (http://www.sipo.gov.cn), 2,500 antihyperglycemic patents for T2DM were identified and analyzed. These consisted of 4 patents for derivatives of herbal extracts (0.2%), 162 patents for herbal extracts (6.5%), 61 compositions for traditional Chinese medicine (TCM) (2.4%), 2,263 patents for synthetic compounds (90.5%), and 10 (0.4%) patents of the combination of synthetic compounds and TCM. As the most common drugs for diabetes mellitus, synthetic compounds can also be classified into several categories according to their working mechanisms, such as insulin secretion promotor agents, insulin sensitizer agents, α-glucosidase inhibitors, and so forth. This article discussed the chemical structure, potential antihyperglycemic mechanism of these antihyperglycemic drugs in patents in China.Expert opinion: Insulin sensitivity and β-cell function could be improved by weight loss to prevent prediabetes into T2DM. However, 40–50% patients with impaired glucose tolerance (IGT) still progress to T2DM, even after successful long-term weight loss.Antihyperglycemic remedies provide a treatment option to improve insulin sensitivity and maintain β-cell function. Combination therapy is the best treatment for diabetes. Combination therapy can reduce the dosage of each single drug option, and avoid the side effects. Drugs with different mechanisms are complementary, and are better adapted to patients with changing conditions. Classical combination therapies include combinations such as sulfonylureas plus biguanides or glucosidase inhibitors, biguanide plus glucosidase inhibitors or insulin sensitizers, insulin treatment plus biguanides or glucosidase inhibitors. The general principle of combination therapy is that two drugs with different mechanisms are selected jointly, and the combination of three types of hypoglycemic drugs is not recommended. After reading a large amount of literature, we have rarely found a case of three oral hypoglycemic agents, which may mean that the combination of three oral hypoglycemic agents is unnecessary and has unpredictable risks. There is no objection to the idea of multi-drug therapy. But multiple drugs can only be used when it shows a significant benefit to the patients. Combined use of multiple antidiabetic drugs poses a risk to patients due to drug interactions and overtreatment